Integrating remote sensing classification techniques for land use mapping in semi-arid regions: a case study of the Tamlouka basin, Algeria.

Worldwide, the majority of countries are actively devising strategies to address the challenges associated with unregulated and unmanageable development, the decline in environmental quality and the depletion of valuable agricultural land. This has led to a growing emphasis on understanding land use and land cover. In order to determine a better land use policy, legislators and planners need to know the current distribution of agricultural and urban lands, as well as information about changes in their proportions. Our approach combines data centred on main four themes-geology, slope gradient, hydrographic network and land use-in order to exploit classifier complementarities in our targeted agricultural study area of Tamlouka Basin, Algeria. Landsat 8 OLI-TIRs multispectral imagery and Shuttle Radar Topography Mission (SRTM-1arc v3) were used experimentally for classification and Digital Elevation Model (DEM) analysis. The classification's accuracy is confirmed by comparing the results of the decision tree classification with the validation samples. Results of the combination of several maps of classifications from the different methods show that the Tamlouka alluvial plain, having an area of 19,300 ha and an average slope gradient of less than 2°, drains the elevated reliefs that surround it via hydrographic network. The plain occupies 37% of the total basin area, with over of 60% being used for crop cultivation, regardless of fallow land areas in agricultural rotation at that time. The slope has been identified as a crucial factor determining land use patterns in the study area. This result can be used in prospective watershed management.

Combined effects of seawater intrusion and nitrate contamination on groundwater in coastal agricultural areas: A case from the Plain of the El-Nil River (North-Eastern Algeria).

This study focuses on coastal aquifers subject to uncontrolled land use development by investigating the combined effects of seawater intrusion and nitrate contamination. The research is undertaken in a Mediterranean coastal agricultural area (Plain of the El-Nil River, Algeria), where water resources are heavily impacted by anthropogenic activities. A multi-tracer approach, integrating hydrogeochemical and isotopic tracers (δ2HH2O, δ18OH2O, δ15NNO3 and δ18ONO3), is combined with a hydrochemical facies evolution diagram, and a Bayesian isotope mixing model (MixSIAR) to assess seawater contamination with its inland intrusion, and distinguish the nitrate sources and their apportionment. Results show that seawater intrusion is circumscribed to the sector neighboring the Mediterranean Sea, with two influencing functions including classic inland intrusion through the aquifer, and upstream seawater impact through the river mouth connected to the Mediterranean Sea. Groundwater and surface water samples reveal nitrate concentrations above the natural baseline threshold, suggesting anthropogenic influence. Results from nitrate isotopic composition, NO3 and Cl concentrations, and the MixSIAR model show that nitrate concentrations chiefly originate from sewage and manure sources. Nitrate derived from the sewage is related to wastewater discharge, whereas nitrate derived from the manure is attributed to an excessive use of animal manure to fertilise agricultural areas. The dual negative impact of seawater intrusion and nitrate contamination degrades water quality over a large proportion of the study area. The outcomes of this study are expected to contribute to effective and sustainable water resources management in the Mediterranean coastal area. Furthermore, this study may improve scientists' ability to predict the combined effect of various anthropogenic stressors on coastal environments and help decision-makers elsewhere to prepare suitable environmental strategies for other regions currently undergoing an early stage of water resources deterioration.